
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase        =				{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				["""BlipImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BlipModel""",
					    """BlipPreTrainedModel""",
					    """BlipForConditionalGeneration""",
					    """BlipForQuestionAnswering""",
					    """BlipVisionModel""",
					    """BlipTextModel""",
					    """BlipForImageTextRetrieval""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFBlipModel""",
					    """TFBlipPreTrainedModel""",
					    """TFBlipForConditionalGeneration""",
					    """TFBlipForQuestionAnswering""",
					    """TFBlipVisionModel""",
					    """TFBlipTextModel""",
					    """TFBlipForImageTextRetrieval""",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						Dict:

	'''simple docstring'''




	def wrapper(*_lowerCamelCase:		Any,					**_lowerCamelCase:		Union[str, Any]			):
		lowerCAmelCase												=  timeit.default_timer()
		lowerCAmelCase												=  func(*_lowerCamelCase,					**_lowerCamelCase			)
		lowerCAmelCase												=  timeit.default_timer() - starttime
		return delta

	lowerCAmelCase												=  func.__name__

	return wrapper




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		List[Any]=100,					_lowerCamelCase:		int=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  seq_shapes or {}
	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  {}
		for col_id, (k, v) in enumerate(features.items()			):
			if isinstance(_lowerCamelCase,					_ArrayXD			):
				lowerCAmelCase												=  np.random.rand(*v.shape			).astype(v.dtype			)
			elif isinstance(_lowerCamelCase,					datasets.Value			):
				if v.dtype == "string":
					lowerCAmelCase												=  '''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCAmelCase												=  np.random.randint(10,					size=1			).astype(v.dtype			).item()
			elif isinstance(_lowerCamelCase,					datasets.Sequence			):
				while isinstance(_lowerCamelCase,					datasets.Sequence			):
					lowerCAmelCase												=  v.feature
				lowerCAmelCase												=  seq_shapes[k]
				lowerCAmelCase												=  np.random.rand(*_lowerCamelCase			).astype(v.dtype			)
			lowerCAmelCase												=  data

		dummy_data.append((i, example)			)

	return dummy_data




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Union[str, Any]=100,					_lowerCamelCase:		List[Any]=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  generate_examples(_lowerCamelCase,					num_examples=_lowerCamelCase,					seq_shapes=_lowerCamelCase			)

	with ArrowWriter(features=_lowerCamelCase,					path=_lowerCamelCase			) as writer:
		for key, record in dummy_data:
			lowerCAmelCase												=  features.encode_example(_lowerCamelCase			)
			writer.write(_lowerCamelCase			)

		lowerCAmelCase							,       lowerCAmelCase												=  writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""			)

	lowerCAmelCase												=  datasets.Dataset.from_file(filename=_lowerCamelCase,					info=datasets.DatasetInfo(features=_lowerCamelCase			)			)

	return dataset


